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RESEARCH PAPER · Journal of Materials Informatics · 2022

Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys

Shengkun Xi, Jinxin Yu, Longke Bao, Liuping Chen, Zhou Li, Rongpei Shi, Cuiping Wang, Xingjun Liu

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Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloysOriginalNew trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems24Machine Learning Design of Aluminum-Lithium Alloys with High Strength5Predicting atomic structure and mechanical properties in quinary L12-Strengthened cobalt-based superalloys using machine learning-driven first-principles calculations6Re Enhancement Effects: Development of a Reaxffnialre Reactive Force Field for Ni-Based Superalloys10Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries16Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning10A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18Quantum chemical modeling of the structure and stability of hydrated and sulfated stannic acid complexes1A critical review on oxidation behavior of Co-based superalloys26Predefined attention-focused mechanism using center-environment features: a machine learning study of alloying effects on the stability of Nb 5 Si 3 alloys8Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21A roadmap from the bond strength to the grain-boundary energies and macro strength of metals15Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation15Phase diagram construction and prediction method based on machine learning algorithms8Enhanced extrapolative machine learning for designing high‐performance multi‐principal‐element superalloys9Tailoring precipitation‐strengthening in Ir‐based ternary alloys: a first‐principles approach to L1 2 phase engineering55Machine learning with center-environment attention mechanism for multi-component Nb alloys5Solute-hydrogen segregation and interaction at γ/γ′ interface of nickel-based single crystal superalloys2A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design2Research Progress in Titanium Alloy Additive Manufacturing Under Different Energy Sources1Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys0

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  1. New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

    Journal of Materials Informatics · 2022-01-01

    Yinghao Zhou, Zehuan Zhang, Dawei Wang, Weicheng Xiao, Jiang Ju, Shaofei Liu, Bo Xiao, Ming Yan, Tao Yang

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  2. Machine Learning Design of Aluminum-Lithium Alloys with High Strength

    Computers, materials & continua/Computers, materials & continua (Print) · 2023-01-01

    Hongxia Wang, Zhiqiang Duan, Qingwei Guo, Yongmei Zhang, Yuhong Zhao

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  3. Predicting atomic structure and mechanical properties in quinary L12-Strengthened cobalt-based superalloys using machine learning-driven first-principles calculations

    Materials Today Communications · 2023-12-05

    Shengkun Xi, Jinxin Yu, Longke Bao, Jiahui Li, Qiuling Tao, Zhou Li, Rongpei Shi, Cuiping Wang, Xingjun Liu

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  4. Re Enhancement Effects: Development of a Reaxffnialre Reactive Force Field for Ni-Based Superalloys

    Acta Materialia · 2024-01-01

    Wan Du, Xue Fan, Bin Xiao, Junxi Sun, Qingqing Wang, Yuchao Tang, Limin Zhang, William A. Goddard, Yi Liu

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  5. Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries

    Journal of Energy Storage · 2024-01-17

    Jiahui Li, Shengkun Xi, Tongxing Lei, Ruijun Yao, Fanshuai Zeng, Junwei Wu, Shaobo Tu, Xingjun Liu

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  6. Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning

    Materials Today Communications · 2024-07-09

    Shuo Wang, Yan Wu, Qian Xu, Yanman Ma, Tianzhu Wang

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  7. A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

    JOM · 2024-10-10

    Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang

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  8. Quantum chemical modeling of the structure and stability of hydrated and sulfated stannic acid complexes

    Chemical Physics · 2024-11-22

    Tatiana S. Zyubina, A. S. Zyubin, R. V. Pisarev, A. V. Pisareva, Yu. A. Dobrovolsky

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  9. A critical review on oxidation behavior of Co-based superalloys

    Chinese Journal of Aeronautics · 2024-12-30

    Chenghao Pei, Qingshuang Ma, Qiuzhi Gao, Yang Yue, Yuhang Du, Hailian Zhang, Huijun Li

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  10. Predefined attention-focused mechanism using center-environment features: a machine learning study of alloying effects on the stability of Nb 5 Si 3 alloys

    Digital Discovery · 2025-01-01

    Yuchao Tang, Bin Xiao, Shuizhou Chen, Quan Qian, Yi Liu

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  11. Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

    npj Computational Materials · 2025-01-10

    Qinghua Wei, Yuanhao Wang, Yang Guo, Tianyuan Li, Shuting Yu, Ziqiang Dong, Tong‐Yi Zhang

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  12. A roadmap from the bond strength to the grain-boundary energies and macro strength of metals

    Nature Communications · 2025-01-13

    Xin Li, Hao Wu, Wang Gao, Qing Jiang

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  13. Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

    Journal of Materials Informatics · 2025-03-13

    Junwei Chen, Yixin Zhang, Jun Peng Luan, Yunying Fan, Zhigang Yu, Bin Liu, Kuo‐Chih Chou

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  14. Phase diagram construction and prediction method based on machine learning algorithms

    Journal of Materials Research and Technology · 2025-03-14

    Shengkun Xi, Jiahui Li, Longke Bao, Rongpei Shi, Haijun Zhang, Xiaoyu Chong, Zhou Li, Cuiping Wang, Xingjun Liu

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  15. Enhanced extrapolative machine learning for designing high‐performance multi‐principal‐element superalloys

    Rare Metals · 2025-07-29

    Qiuling Tao, Longke Bao, Jiawen Cao, Rongpei Shi, Yilu Zhao, Tao Yang, Xue Jia, Zhifu Yao, Xingjun Liu

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  16. Tailoring precipitation‐strengthening in Ir‐based ternary alloys: a first‐principles approach to L1 2 phase engineering

    Rare Metals · 2025-08-25

    Xian‐Pei Jiang, Wei Yu, Wei Yan, Haijun Wu, Ji‐Ping Ding, Changyi Hu, Xingjun Liu, Jing Feng, Xiaoyu Chong

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  17. Machine learning with center-environment attention mechanism for multi-component Nb alloys

    Transactions of Nonferrous Metals Society of China · 2025-11-01

    Yu-chao TANG, Bin XIAO, Jian-hui CHEN, Shui-zhou CHEN, Yi-hang LI, Fu LIU, Wan DU, Yi-heng SHEN, Xue FAN, Quan QIAN, Yi LIU

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  18. Solute-hydrogen segregation and interaction at γ/γ′ interface of nickel-based single crystal superalloys

    Materials Science and Engineering A · 2025-12-31

    Tingting Zhao, Guangxian Lu, William Yi Wang, Yunsong Zhao, Yushi Luo, Jinshan Li

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  19. A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design

    Progress in Natural Science Materials International · 2026-02-01

    Linlin Sun, Jie Xiong, Qingshuang Ma, Chenghao Pei, Huijun Li, Qiuzhi Gao

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  20. Research Progress in Titanium Alloy Additive Manufacturing Under Different Energy Sources

    Coatings · 2026-05-28

    Guoqun Wu, Rui Wang, Mengxia Ji, Qiuqiao Jiang, Ruoyu Wang, Jieren Guan, Wei Lin

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  21. Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys

    ACS Applied Materials & Interfaces · 2026-08-24

    LiQin Qin, Yuchao Tang, Limin Zhang, Bin Xiao, Wenyi Huo, Yi Liu

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